



'''simple docstring'''






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       (DDPMScheduler,)







    def      lowercase_			( self					:    Optional[int]		,     **__snake_case					:    Optional[int]      ):
      a		:   Optional[Any]	  =	{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**__snake_case      )
      return config







    def      lowercase_			( self					:    Optional[Any]      ):
      for timesteps in [1, 5, 1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=__snake_case      )







    def      lowercase_			( self					:    List[str]      ):
      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,     [0.002, 0.02, 0.2, 2]      ):
        self.check_over_configs(beta_start=__snake_case		,     beta_end=__snake_case      )







    def      lowercase_			( self					:    int      ):
      for schedule in ["linear", "squaredcos_cap_v2"]:
        self.check_over_configs(beta_schedule=__snake_case      )







    def      lowercase_			( self					:    Dict      ):
      for variance in ["fixed_small", "fixed_large", "other"]:
        self.check_over_configs(variance_type=__snake_case      )







    def      lowercase_			( self					:    Dict      ):
      for clip_sample in [True, False]:
        self.check_over_configs(clip_sample=__snake_case      )







    def      lowercase_			( self					:    List[str]      ):
      self.check_over_configs(thresholding=__snake_case      )
      for threshold in [0.5, 1.0, 2.0]:
        for prediction_type in ["epsilon", "sample", "v_prediction"]:
          self.check_over_configs(
              thresholding=__snake_case		,     prediction_type=__snake_case		,     sample_max_value=__snake_case		,     )







    def      lowercase_			( self					:    Union[str, Any]      ):
      for prediction_type in ["epsilon", "sample", "v_prediction"]:
        self.check_over_configs(prediction_type=__snake_case      )







    def      lowercase_			( self					:    str      ):
      for t in [0, 5_00, 9_99]:
        self.check_over_forward(time_step=__snake_case      )







    def      lowercase_			( self					:    List[Any]      ):
      a		:   int	  =	self.scheduler_classes[0]
      a		:   Tuple	  =	self.get_scheduler_config()
      a		:   int	  =	scheduler_class(**__snake_case      )

      assert torch.sum(torch.abs(scheduler._get_variance(0      ) - 0.0      )      ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87      ) - 0.00979      )      ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99      ) - 0.02      )      ) < 1e-5







    def      lowercase_			( self					:    Dict      ):
      a		:   Tuple	  =	self.scheduler_classes[0]
      a		:   List[Any]	  =	self.get_scheduler_config()
      a		:   Any	  =	scheduler_class(**__snake_case      )

      a		:   Union[str, Any]	  =	len(__snake_case      )

      a		:   Optional[Any]	  =	self.dummy_model()
      a		:   Tuple	  =	self.dummy_sample_deter
      a		:   Union[str, Any]	  =	torch.manual_seed(0      )

      for t in reversed(range(__snake_case      )      ):
        # 1. predict noise residual
        a		:   Optional[Any]	  =	model(__snake_case		,     __snake_case      )

        # 2. predict previous mean of sample x_t-1
        a		:   Tuple	  =	scheduler.step(__snake_case		,     __snake_case		,     __snake_case		,     generator=__snake_case      ).prev_sample

        # if t > 0:
        #     noise = self.dummy_sample_deter
        #     variance = scheduler.get_variance(t) ** (0.5) * noise
        #
        # sample = pred_prev_sample + variance
        a		:   List[Any]	  =	pred_prev_sample

      a		:   int	  =	torch.sum(torch.abs(__snake_case      )      )
      a		:   Optional[Any]	  =	torch.mean(torch.abs(__snake_case      )      )

      assert abs(result_sum.item() - 258.9606      ) < 1e-2
      assert abs(result_mean.item() - 0.3372      ) < 1e-3







    def      lowercase_			( self					:    List[str]      ):
      a		:   Tuple	  =	self.scheduler_classes[0]
      a		:   int	  =	self.get_scheduler_config(prediction_type='v_prediction'      )
      a		:   List[Any]	  =	scheduler_class(**__snake_case      )

      a		:   str	  =	len(__snake_case      )

      a		:   Optional[int]	  =	self.dummy_model()
      a		:   List[Any]	  =	self.dummy_sample_deter
      a		:   str	  =	torch.manual_seed(0      )

      for t in reversed(range(__snake_case      )      ):
        # 1. predict noise residual
        a		:   List[Any]	  =	model(__snake_case		,     __snake_case      )

        # 2. predict previous mean of sample x_t-1
        a		:   Tuple	  =	scheduler.step(__snake_case		,     __snake_case		,     __snake_case		,     generator=__snake_case      ).prev_sample

        # if t > 0:
        #     noise = self.dummy_sample_deter
        #     variance = scheduler.get_variance(t) ** (0.5) * noise
        #
        # sample = pred_prev_sample + variance
        a		:   List[str]	  =	pred_prev_sample

      a		:   Dict	  =	torch.sum(torch.abs(__snake_case      )      )
      a		:   Optional[int]	  =	torch.mean(torch.abs(__snake_case      )      )

      assert abs(result_sum.item() - 202.0296      ) < 1e-2
      assert abs(result_mean.item() - 0.2631      ) < 1e-3







    def      lowercase_			( self					:    List[str]      ):
      a		:   Any	  =	self.scheduler_classes[0]
      a		:   List[Any]	  =	self.get_scheduler_config()
      a		:   Tuple	  =	scheduler_class(**__snake_case      )

      a		:   Dict	  =	[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=__snake_case      )

      a		:   Optional[Any]	  =	scheduler.timesteps

      for i, timestep in enumerate(__snake_case      ):
        if i == len(__snake_case      ) - 1:
          a		:   int	  =	-1
        else:
          a		:   List[str]	  =	timesteps[i + 1]

        a		:   Union[str, Any]	  =	scheduler.previous_timestep(__snake_case      )
        a		:   str	  =	prev_t.item()

        self.assertEqual(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    str      ):
      a		:   List[Any]	  =	self.scheduler_classes[0]
      a		:   List[Any]	  =	self.get_scheduler_config()
      a		:   Dict	  =	scheduler_class(**__snake_case      )

      a		:   int	  =	[1_00, 87, 50, 51, 0]

      with self.assertRaises(__snake_case		,     msg='`custom_timesteps` must be in descending order.'      ):
        scheduler.set_timesteps(timesteps=__snake_case      )







    def      lowercase_			( self					:    Any      ):
      a		:   Tuple	  =	self.scheduler_classes[0]
      a		:   Dict	  =	self.get_scheduler_config()
      a		:   str	  =	scheduler_class(**__snake_case      )

      a		:   str	  =	[1_00, 87, 50, 1, 0]
      a		:   Dict	  =	len(__snake_case      )

      with self.assertRaises(__snake_case		,     msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'      ):
        scheduler.set_timesteps(num_inference_steps=__snake_case		,     timesteps=__snake_case      )







    def      lowercase_			( self					:    int      ):
      a		:   Union[str, Any]	  =	self.scheduler_classes[0]
      a		:   Optional[Any]	  =	self.get_scheduler_config()
      a		:   List[str]	  =	scheduler_class(**__snake_case      )

      a		:   Dict	  =	[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          __snake_case		,     msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'		,     ):
        scheduler.set_timesteps(timesteps=__snake_case      )




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase:   List[Any]					=		{'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Dict					=		['ReformerTokenizer']

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   Tuple					=		['ReformerTokenizerFast']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase:   List[Any]					=		[
         'REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'ReformerAttention',
         'ReformerForMaskedLM',
         'ReformerForQuestionAnswering',
         'ReformerForSequenceClassification',
         'ReformerLayer',
         'ReformerModel',
         'ReformerModelWithLMHead',
         'ReformerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer import ReformerTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_reformer_fast import ReformerTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_reformer import (
              REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              ReformerAttention,
              ReformerForMaskedLM,
              ReformerForQuestionAnswering,
              ReformerForSequenceClassification,
              ReformerLayer,
              ReformerModel,
              ReformerModelWithLMHead,
              ReformerPreTrainedModel,
          )

else:
     import sys

     lowerCAmelCase:   str					=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)